Intelligent Detection of Missing and Unattended Objects in Complex Scene of Surveillance Videos

Abstract

This study proposes a method to detect and mark the target object removed from the monitoring scene and the unknown object left in the monitoring scene. The present method uses the timeliness background to extract the foreground object and to mask the part that was unwanted. The foreground object was compared with the current frame, thus, the unreliable pixels were filtered out. By the identification of the center of mass (CoM) on foreground object, an object detection rule is developed to determine whether the foreground object is missing object or unattended object. In this paper, the present approach improves the problem with the high similarity of pixels between the foreground object and the background model. The experiment can be applied to any complex environment, both indoors and outdoors, such as the subway station, which is thronged with people. The experimental outcome, using the proposed method, can determine the missing and unattended object accurately and the unreasonable object is excluded in video surveillance system.

Recognition of Suspicious Human Activity in Video Surveillance: A Review

“…However, identification of abandoned/unattended object is very difficult in video frames in crowded environment. To tackle the problem different techniques have been proposed, such as frame differencing, optical flow, and background subtraction [9,10].…”

Detection of stationary foreground objects: A survey

“…A hybrid differencing-based strategy at region level is proposed in [76]. The authors of [84] propose using a timeliness BG that uses real world time instead of the frames. The BG modeling strategy in [85], avoids making assumptions of normality pixel distributions and uses the Chebyschev probability inequality.…”

“…Additionally, many of them allow detecting PSFOs [25,71,84,87]. Moreover, since ROs give rise to uncovered BG, these strategies have the capability of correctly detecting these kind of objects.…”

“…Moreover, since ROs give rise to uncovered BG, these strategies have the capability of correctly detecting these kind of objects. However, only some of the methods using DFCs focuse on separating abandoned objects from the removed ones [71,84,101].…”

“…Many of these specific mechanisms [8,31,40,62,83,84,101] rely on the analysis of the edges of both the current image and a BG image (point estimate of the BG that is typically obtained from the BG model). The intuition is that, in many cases, if the BG is covered with a A C C E P T E D M A N U S C R I P T SFO the number of edges in the image will grow.…”